Different from keyphrase extraction, keyphrase generation models could generate absent keyphrases by modeling such task as a sequence-to-sequence (seq2seq) learning problem.
[1]} first proposed CopyRNN, a seq2seq framework with copy mechanism for keyphrase generation.
Following this framework, many enhanced variants of CopyRNN are proposed [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}.
For example,
[2]} proposed a semi-supervised methods by leveraging both labeled and unlabeled data.
[3]} proposed TG-Net taking title information into consideration.
[5]} utilized reinforcement learning with adaptive rewards to generate more sufficient and accurate keyphrase.
[7]} designed a hierarchical decoding process and an exclusion mechanism to avoid generating duplicated keyphrases.
Moreover, [13]}, [14]} proposed a keyphrase generation approach using conditional Generative Adversarial Networks.
[15]} proposed a new training paradigm ONE2SET without predefining an order to concatenate the keyphrases.
[16]} proposed a method for neural keyphrase generation with layer-wise coverage attention.
